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<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.html" title="class in org.apache.spark.streaming.api.java">JavaPairReceiverInputDStream</a><java.lang.String,java.lang.String></code></td>
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<div class="block">Create an input stream that pulls messages from Kafka Brokers.</div>
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<td class="colFirst"><code>static <a href="../../../../../org/apache/spark/streaming/api/java/JavaPairReceiverInputDStream.html" title="class in org.apache.spark.streaming.api.java">JavaPairReceiverInputDStream</a><java.lang.String,java.lang.String></code></td>
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<td class="colFirst"><code>static <K,V,U extends kafka.serializer.Decoder<?>,T extends kafka.serializer.Decoder<?>> <br><a href="../../../../../org/apache/spark/streaming/dstream/ReceiverInputDStream.html" title="class in org.apache.spark.streaming.dstream">ReceiverInputDStream</a><scala.Tuple2<K,V>></code></td>
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java.lang.String zkQuorum,
java.lang.String groupId,
scala.collection.immutable.Map<java.lang.String,java.lang.Object> topics,
<a href="../../../../../org/apache/spark/storage/StorageLevel.html" title="class in org.apache.spark.storage">StorageLevel</a> storageLevel)</pre>
<div class="block">Create an input stream that pulls messages from Kafka Brokers.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>ssc</code> - StreamingContext object</dd><dd><code>zkQuorum</code> - Zookeeper quorum (hostname:port,hostname:port,..)</dd><dd><code>groupId</code> - The group id for this consumer</dd><dd><code>topics</code> - Map of (topic_name -> numPartitions) to consume. Each partition is consumed
in its own thread</dd><dd><code>storageLevel</code> - Storage level to use for storing the received objects
(default: StorageLevel.MEMORY_AND_DISK_SER_2)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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scala.collection.immutable.Map<java.lang.String,java.lang.String> kafkaParams,
scala.collection.immutable.Map<java.lang.String,java.lang.Object> topics,
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scala.reflect.ClassTag<V> evidence$2,
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<div class="block">Create an input stream that pulls messages from Kafka Brokers.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>ssc</code> - StreamingContext object</dd><dd><code>kafkaParams</code> - Map of kafka configuration parameters,
see http://kafka.apache.org/08/configuration.html</dd><dd><code>topics</code> - Map of (topic_name -> numPartitions) to consume. Each partition is consumed
in its own thread.</dd><dd><code>storageLevel</code> - Storage level to use for storing the received objects</dd><dd><code>evidence$1</code> - (undocumented)</dd><dd><code>evidence$2</code> - (undocumented)</dd><dd><code>evidence$3</code> - (undocumented)</dd><dd><code>evidence$4</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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java.lang.String zkQuorum,
java.lang.String groupId,
java.util.Map<java.lang.String,java.lang.Integer> topics)</pre>
<div class="block">Create an input stream that pulls messages from Kafka Brokers.
Storage level of the data will be the default StorageLevel.MEMORY_AND_DISK_SER_2.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>jssc</code> - JavaStreamingContext object</dd><dd><code>zkQuorum</code> - Zookeeper quorum (hostname:port,hostname:port,..)</dd><dd><code>groupId</code> - The group id for this consumer</dd><dd><code>topics</code> - Map of (topic_name -> numPartitions) to consume. Each partition is consumed
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<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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java.lang.String zkQuorum,
java.lang.String groupId,
java.util.Map<java.lang.String,java.lang.Integer> topics,
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<div class="block">Create an input stream that pulls messages from Kafka Brokers.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>jssc</code> - JavaStreamingContext object</dd><dd><code>zkQuorum</code> - Zookeeper quorum (hostname:port,hostname:port,..).</dd><dd><code>groupId</code> - The group id for this consumer.</dd><dd><code>topics</code> - Map of (topic_name -> numPartitions) to consume. Each partition is consumed
in its own thread.</dd><dd><code>storageLevel</code> - RDD storage level.</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<div class="block">Create an input stream that pulls messages from Kafka Brokers.</div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>jssc</code> - JavaStreamingContext object</dd><dd><code>keyTypeClass</code> - Key type of DStream</dd><dd><code>valueTypeClass</code> - value type of Dstream</dd><dd><code>keyDecoderClass</code> - Type of kafka key decoder</dd><dd><code>valueDecoderClass</code> - Type of kafka value decoder</dd><dd><code>kafkaParams</code> - Map of kafka configuration parameters,
see http://kafka.apache.org/08/configuration.html</dd><dd><code>topics</code> - Map of (topic_name -> numPartitions) to consume. Each partition is consumed
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<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<div class="block">Create a RDD from Kafka using offset ranges for each topic and partition.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>sc</code> - SparkContext object</dd><dd><code>kafkaParams</code> - Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
to be set with Kafka broker(s) (NOT zookeeper servers) specified in
host1:port1,host2:port2 form.</dd><dd><code>offsetRanges</code> - Each OffsetRange in the batch corresponds to a
range of offsets for a given Kafka topic/partition</dd><dd><code>evidence$5</code> - (undocumented)</dd><dd><code>evidence$6</code> - (undocumented)</dd><dd><code>evidence$7</code> - (undocumented)</dd><dd><code>evidence$8</code> - (undocumented)</dd>
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scala.reflect.ClassTag<KD> evidence$11,
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<div class="block">Create a RDD from Kafka using offset ranges for each topic and partition. This allows you
specify the Kafka leader to connect to (to optimize fetching) and access the message as well
as the metadata.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>sc</code> - SparkContext object</dd><dd><code>kafkaParams</code> - Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
to be set with Kafka broker(s) (NOT zookeeper servers) specified in
host1:port1,host2:port2 form.</dd><dd><code>offsetRanges</code> - Each OffsetRange in the batch corresponds to a
range of offsets for a given Kafka topic/partition</dd><dd><code>leaders</code> - Kafka brokers for each TopicAndPartition in offsetRanges. May be an empty map,
in which case leaders will be looked up on the driver.</dd><dd><code>messageHandler</code> - Function for translating each message and metadata into the desired type</dd><dd><code>evidence$9</code> - (undocumented)</dd><dd><code>evidence$10</code> - (undocumented)</dd><dd><code>evidence$11</code> - (undocumented)</dd><dd><code>evidence$12</code> - (undocumented)</dd><dd><code>evidence$13</code> - (undocumented)</dd>
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<div class="block">Create a RDD from Kafka using offset ranges for each topic and partition.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>jsc</code> - JavaSparkContext object</dd><dd><code>kafkaParams</code> - Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
to be set with Kafka broker(s) (NOT zookeeper servers) specified in
host1:port1,host2:port2 form.</dd><dd><code>offsetRanges</code> - Each OffsetRange in the batch corresponds to a
range of offsets for a given Kafka topic/partition</dd><dd><code>keyClass</code> - (undocumented)</dd><dd><code>valueClass</code> - (undocumented)</dd><dd><code>keyDecoderClass</code> - (undocumented)</dd><dd><code>valueDecoderClass</code> - (undocumented)</dd>
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java.lang.Class<V> valueClass,
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java.lang.Class<R> recordClass,
java.util.Map<java.lang.String,java.lang.String> kafkaParams,
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<div class="block">Create a RDD from Kafka using offset ranges for each topic and partition. This allows you
specify the Kafka leader to connect to (to optimize fetching) and access the message as well
as the metadata.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>jsc</code> - JavaSparkContext object</dd><dd><code>kafkaParams</code> - Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
to be set with Kafka broker(s) (NOT zookeeper servers) specified in
host1:port1,host2:port2 form.</dd><dd><code>offsetRanges</code> - Each OffsetRange in the batch corresponds to a
range of offsets for a given Kafka topic/partition</dd><dd><code>leaders</code> - Kafka brokers for each TopicAndPartition in offsetRanges. May be an empty map,
in which case leaders will be looked up on the driver.</dd><dd><code>messageHandler</code> - Function for translating each message and metadata into the desired type</dd><dd><code>keyClass</code> - (undocumented)</dd><dd><code>valueClass</code> - (undocumented)</dd><dd><code>keyDecoderClass</code> - (undocumented)</dd><dd><code>valueDecoderClass</code> - (undocumented)</dd><dd><code>recordClass</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
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<pre>public static <K,V,KD extends kafka.serializer.Decoder<K>,VD extends kafka.serializer.Decoder<V>,R> <a href="../../../../../org/apache/spark/streaming/dstream/InputDStream.html" title="class in org.apache.spark.streaming.dstream">InputDStream</a><R> createDirectStream(<a href="../../../../../org/apache/spark/streaming/StreamingContext.html" title="class in org.apache.spark.streaming">StreamingContext</a> ssc,
scala.collection.immutable.Map<java.lang.String,java.lang.String> kafkaParams,
scala.collection.immutable.Map<kafka.common.TopicAndPartition,java.lang.Object> fromOffsets,
scala.Function1<kafka.message.MessageAndMetadata<K,V>,R> messageHandler,
scala.reflect.ClassTag<K> evidence$14,
scala.reflect.ClassTag<V> evidence$15,
scala.reflect.ClassTag<KD> evidence$16,
scala.reflect.ClassTag<VD> evidence$17,
scala.reflect.ClassTag<R> evidence$18)</pre>
<div class="block">Create an input stream that directly pulls messages from Kafka Brokers
without using any receiver. This stream can guarantee that each message
from Kafka is included in transformations exactly once (see points below).
<p>
Points to note:
- No receivers: This stream does not use any receiver. It directly queries Kafka
- Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
by the stream itself. For interoperability with Kafka monitoring tools that depend on
Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
You can access the offsets used in each batch from the generated RDDs (see
<a href="../../../../../org/apache/spark/streaming/kafka/HasOffsetRanges.html" title="interface in org.apache.spark.streaming.kafka"><code>HasOffsetRanges</code></a>).
- Failure Recovery: To recover from driver failures, you have to enable checkpointing
in the <code>StreamingContext</code>. The information on consumed offset can be
recovered from the checkpoint. See the programming guide for details (constraints, etc.).
- End-to-end semantics: This stream ensures that every records is effectively received and
transformed exactly once, but gives no guarantees on whether the transformed data are
outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure
that the output operation is idempotent, or use transactions to output records atomically.
See the programming guide for more details.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>ssc</code> - StreamingContext object</dd><dd><code>kafkaParams</code> - Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
to be set with Kafka broker(s) (NOT zookeeper servers) specified in
host1:port1,host2:port2 form.</dd><dd><code>fromOffsets</code> - Per-topic/partition Kafka offsets defining the (inclusive)
starting point of the stream</dd><dd><code>messageHandler</code> - Function for translating each message and metadata into the desired type</dd><dd><code>evidence$14</code> - (undocumented)</dd><dd><code>evidence$15</code> - (undocumented)</dd><dd><code>evidence$16</code> - (undocumented)</dd><dd><code>evidence$17</code> - (undocumented)</dd><dd><code>evidence$18</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
<a name="createDirectStream(org.apache.spark.streaming.StreamingContext, scala.collection.immutable.Map, scala.collection.immutable.Set, scala.reflect.ClassTag, scala.reflect.ClassTag, scala.reflect.ClassTag, scala.reflect.ClassTag)">
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<h4>createDirectStream</h4>
<pre>public static <K,V,KD extends kafka.serializer.Decoder<K>,VD extends kafka.serializer.Decoder<V>> <a href="../../../../../org/apache/spark/streaming/dstream/InputDStream.html" title="class in org.apache.spark.streaming.dstream">InputDStream</a><scala.Tuple2<K,V>> createDirectStream(<a href="../../../../../org/apache/spark/streaming/StreamingContext.html" title="class in org.apache.spark.streaming">StreamingContext</a> ssc,
scala.collection.immutable.Map<java.lang.String,java.lang.String> kafkaParams,
scala.collection.immutable.Set<java.lang.String> topics,
scala.reflect.ClassTag<K> evidence$19,
scala.reflect.ClassTag<V> evidence$20,
scala.reflect.ClassTag<KD> evidence$21,
scala.reflect.ClassTag<VD> evidence$22)</pre>
<div class="block">Create an input stream that directly pulls messages from Kafka Brokers
without using any receiver. This stream can guarantee that each message
from Kafka is included in transformations exactly once (see points below).
<p>
Points to note:
- No receivers: This stream does not use any receiver. It directly queries Kafka
- Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
by the stream itself. For interoperability with Kafka monitoring tools that depend on
Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
You can access the offsets used in each batch from the generated RDDs (see
<a href="../../../../../org/apache/spark/streaming/kafka/HasOffsetRanges.html" title="interface in org.apache.spark.streaming.kafka"><code>HasOffsetRanges</code></a>).
- Failure Recovery: To recover from driver failures, you have to enable checkpointing
in the <code>StreamingContext</code>. The information on consumed offset can be
recovered from the checkpoint. See the programming guide for details (constraints, etc.).
- End-to-end semantics: This stream ensures that every records is effectively received and
transformed exactly once, but gives no guarantees on whether the transformed data are
outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure
that the output operation is idempotent, or use transactions to output records atomically.
See the programming guide for more details.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>ssc</code> - StreamingContext object</dd><dd><code>kafkaParams</code> - Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
to be set with Kafka broker(s) (NOT zookeeper servers), specified in
host1:port1,host2:port2 form.
If not starting from a checkpoint, "auto.offset.reset" may be set to "largest" or "smallest"
to determine where the stream starts (defaults to "largest")</dd><dd><code>topics</code> - Names of the topics to consume</dd><dd><code>evidence$19</code> - (undocumented)</dd><dd><code>evidence$20</code> - (undocumented)</dd><dd><code>evidence$21</code> - (undocumented)</dd><dd><code>evidence$22</code> - (undocumented)</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
<a name="createDirectStream(org.apache.spark.streaming.api.java.JavaStreamingContext, java.lang.Class, java.lang.Class, java.lang.Class, java.lang.Class, java.lang.Class, java.util.Map, java.util.Map, org.apache.spark.api.java.function.Function)">
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<h4>createDirectStream</h4>
<pre>public static <K,V,KD extends kafka.serializer.Decoder<K>,VD extends kafka.serializer.Decoder<V>,R> <a href="../../../../../org/apache/spark/streaming/api/java/JavaInputDStream.html" title="class in org.apache.spark.streaming.api.java">JavaInputDStream</a><R> createDirectStream(<a href="../../../../../org/apache/spark/streaming/api/java/JavaStreamingContext.html" title="class in org.apache.spark.streaming.api.java">JavaStreamingContext</a> jssc,
java.lang.Class<K> keyClass,
java.lang.Class<V> valueClass,
java.lang.Class<KD> keyDecoderClass,
java.lang.Class<VD> valueDecoderClass,
java.lang.Class<R> recordClass,
java.util.Map<java.lang.String,java.lang.String> kafkaParams,
java.util.Map<kafka.common.TopicAndPartition,java.lang.Long> fromOffsets,
<a href="../../../../../org/apache/spark/api/java/function/Function.html" title="interface in org.apache.spark.api.java.function">Function</a><kafka.message.MessageAndMetadata<K,V>,R> messageHandler)</pre>
<div class="block">Create an input stream that directly pulls messages from Kafka Brokers
without using any receiver. This stream can guarantee that each message
from Kafka is included in transformations exactly once (see points below).
<p>
Points to note:
- No receivers: This stream does not use any receiver. It directly queries Kafka
- Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
by the stream itself. For interoperability with Kafka monitoring tools that depend on
Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
You can access the offsets used in each batch from the generated RDDs (see
<a href="../../../../../org/apache/spark/streaming/kafka/HasOffsetRanges.html" title="interface in org.apache.spark.streaming.kafka"><code>HasOffsetRanges</code></a>).
- Failure Recovery: To recover from driver failures, you have to enable checkpointing
in the <code>StreamingContext</code>. The information on consumed offset can be
recovered from the checkpoint. See the programming guide for details (constraints, etc.).
- End-to-end semantics: This stream ensures that every records is effectively received and
transformed exactly once, but gives no guarantees on whether the transformed data are
outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure
that the output operation is idempotent, or use transactions to output records atomically.
See the programming guide for more details.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>jssc</code> - JavaStreamingContext object</dd><dd><code>keyClass</code> - Class of the keys in the Kafka records</dd><dd><code>valueClass</code> - Class of the values in the Kafka records</dd><dd><code>keyDecoderClass</code> - Class of the key decoder</dd><dd><code>valueDecoderClass</code> - Class of the value decoder</dd><dd><code>recordClass</code> - Class of the records in DStream</dd><dd><code>kafkaParams</code> - Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
to be set with Kafka broker(s) (NOT zookeeper servers), specified in
host1:port1,host2:port2 form.</dd><dd><code>fromOffsets</code> - Per-topic/partition Kafka offsets defining the (inclusive)
starting point of the stream</dd><dd><code>messageHandler</code> - Function for translating each message and metadata into the desired type</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
<a name="createDirectStream(org.apache.spark.streaming.api.java.JavaStreamingContext, java.lang.Class, java.lang.Class, java.lang.Class, java.lang.Class, java.util.Map, java.util.Set)">
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<ul class="blockListLast">
<li class="blockList">
<h4>createDirectStream</h4>
<pre>public static <K,V,KD extends kafka.serializer.Decoder<K>,VD extends kafka.serializer.Decoder<V>> <a href="../../../../../org/apache/spark/streaming/api/java/JavaPairInputDStream.html" title="class in org.apache.spark.streaming.api.java">JavaPairInputDStream</a><K,V> createDirectStream(<a href="../../../../../org/apache/spark/streaming/api/java/JavaStreamingContext.html" title="class in org.apache.spark.streaming.api.java">JavaStreamingContext</a> jssc,
java.lang.Class<K> keyClass,
java.lang.Class<V> valueClass,
java.lang.Class<KD> keyDecoderClass,
java.lang.Class<VD> valueDecoderClass,
java.util.Map<java.lang.String,java.lang.String> kafkaParams,
java.util.Set<java.lang.String> topics)</pre>
<div class="block">Create an input stream that directly pulls messages from Kafka Brokers
without using any receiver. This stream can guarantee that each message
from Kafka is included in transformations exactly once (see points below).
<p>
Points to note:
- No receivers: This stream does not use any receiver. It directly queries Kafka
- Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
by the stream itself. For interoperability with Kafka monitoring tools that depend on
Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
You can access the offsets used in each batch from the generated RDDs (see
<a href="../../../../../org/apache/spark/streaming/kafka/HasOffsetRanges.html" title="interface in org.apache.spark.streaming.kafka"><code>HasOffsetRanges</code></a>).
- Failure Recovery: To recover from driver failures, you have to enable checkpointing
in the <code>StreamingContext</code>. The information on consumed offset can be
recovered from the checkpoint. See the programming guide for details (constraints, etc.).
- End-to-end semantics: This stream ensures that every records is effectively received and
transformed exactly once, but gives no guarantees on whether the transformed data are
outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure
that the output operation is idempotent, or use transactions to output records atomically.
See the programming guide for more details.
<p></div>
<dl><dt><span class="strong">Parameters:</span></dt><dd><code>jssc</code> - JavaStreamingContext object</dd><dd><code>keyClass</code> - Class of the keys in the Kafka records</dd><dd><code>valueClass</code> - Class of the values in the Kafka records</dd><dd><code>keyDecoderClass</code> - Class of the key decoder</dd><dd><code>valueDecoderClass</code> - Class type of the value decoder</dd><dd><code>kafkaParams</code> - Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
to be set with Kafka broker(s) (NOT zookeeper servers), specified in
host1:port1,host2:port2 form.
If not starting from a checkpoint, "auto.offset.reset" may be set to "largest" or "smallest"
to determine where the stream starts (defaults to "largest")</dd><dd><code>topics</code> - Names of the topics to consume</dd>
<dt><span class="strong">Returns:</span></dt><dd>(undocumented)</dd></dl>
</li>
</ul>
</li>
</ul>
</li>
</ul>
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